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Mapping active paddy rice area over monsoon Asia using time-series sentinel – 2 images in Google earth engine; a case study over Lower Gangetic Plain

Mapping active paddy rice area over monsoon Asia using time-series sentinel – 2 images in Google earth engine; a case study over Lower Gangetic Plain
Mapping active paddy rice area over monsoon Asia using time-series sentinel – 2 images in Google earth engine; a case study over Lower Gangetic Plain
We proposed a modification of the existing approach for mapping active paddy rice fields in monsoon-dominated areas. In the existing PPPM approach, LSWI higher than EVI at the transplantation stage enables the identification of rice fields. However, it fails to recognize the fields submerged later due to monsoon floods. In the proposed approach (IPPPM), the submerged fields, at the maximum greenness time, were excluded for better estimation. Sentinel–2A/2B time-series images were used for the year 2018 to map paddy rice over the Lower Gangetic Plain (LGP) using Google earth engine (GEE). The overall accuracy (OA) obtained from IPPPM was 85%. Further comparison with the statistical data reveals the IPPPM underestimates (slope (β1) = 0.77) the total reported paddy rice area, though R2 remains close to 0.9. The findings provide a basis for near real-time mapping of active paddy rice areas for addressing the issues of production and food security.
Google earth engine, lower gangetic plain, Paddy rice mapping, random forest, Sentinel-2
1010-6049
10254-10277
Maiti, Arabinda
fb60d92e-e7ad-4419-bbd2-061651122329
Acharya, Prasenjit
10112fa6-2292-4673-ad17-2888d5e76893
Sannigrahi, Srikanta
a30e7158-fbf1-4ce1-a728-1c4a4045e3f9
Zhang, Qi
f7dc07ab-ebfd-4ade-b85d-134b8d534455
Bar, Somnath
1e199d14-4020-46ef-9dfa-733fe5fa6082
Chakraborti, Suman
5b4c47d1-def7-4460-8630-3b1937206e84
Gayen, Bijoy K.
f68ed77e-de89-4cae-bf52-60b918669c34
Barik, Gunadhar
f1ac8509-2f44-4c96-880c-2f79b90adc05
Ghosh, Surajit
54ead9fb-64a0-4ed2-8f8b-1d393b33460e
Punia, Milap
9520da18-bc44-460d-b104-5c998be0cb24
et al.
Maiti, Arabinda
fb60d92e-e7ad-4419-bbd2-061651122329
Acharya, Prasenjit
10112fa6-2292-4673-ad17-2888d5e76893
Sannigrahi, Srikanta
a30e7158-fbf1-4ce1-a728-1c4a4045e3f9
Zhang, Qi
f7dc07ab-ebfd-4ade-b85d-134b8d534455
Bar, Somnath
1e199d14-4020-46ef-9dfa-733fe5fa6082
Chakraborti, Suman
5b4c47d1-def7-4460-8630-3b1937206e84
Gayen, Bijoy K.
f68ed77e-de89-4cae-bf52-60b918669c34
Barik, Gunadhar
f1ac8509-2f44-4c96-880c-2f79b90adc05
Ghosh, Surajit
54ead9fb-64a0-4ed2-8f8b-1d393b33460e
Punia, Milap
9520da18-bc44-460d-b104-5c998be0cb24

Maiti, Arabinda, Acharya, Prasenjit, Sannigrahi, Srikanta and Bar, Somnath , et al. (2022) Mapping active paddy rice area over monsoon Asia using time-series sentinel – 2 images in Google earth engine; a case study over Lower Gangetic Plain. Geocarto International, 37 (25), 10254-10277. (doi:10.1080/10106049.2022.2032396).

Record type: Article

Abstract

We proposed a modification of the existing approach for mapping active paddy rice fields in monsoon-dominated areas. In the existing PPPM approach, LSWI higher than EVI at the transplantation stage enables the identification of rice fields. However, it fails to recognize the fields submerged later due to monsoon floods. In the proposed approach (IPPPM), the submerged fields, at the maximum greenness time, were excluded for better estimation. Sentinel–2A/2B time-series images were used for the year 2018 to map paddy rice over the Lower Gangetic Plain (LGP) using Google earth engine (GEE). The overall accuracy (OA) obtained from IPPPM was 85%. Further comparison with the statistical data reveals the IPPPM underestimates (slope (β1) = 0.77) the total reported paddy rice area, though R2 remains close to 0.9. The findings provide a basis for near real-time mapping of active paddy rice areas for addressing the issues of production and food security.

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More information

Accepted/In Press date: 17 January 2022
e-pub ahead of print date: 8 February 2022
Additional Information: Funding Information: This work was work supported by Microsoft Research and University Grants Commission. The first author is thankful to the University Grants Commission, Government of India, for financial support (UGC Ref. No. 3265(NET-JULY2016). We acknowledge the Department of Science and Technology, Government of India, for providing funds (SR/FST/ES-I/2017/7/2017) to the Department of Geography, Vidyasagar University. Qi Zhang's collaboration was partially supported by a Microsoft AI for Earth Compute Grant and an American Association of Geographers (AAG) Research Grant.
Keywords: Google earth engine, lower gangetic plain, Paddy rice mapping, random forest, Sentinel-2

Identifiers

Local EPrints ID: 485178
URI: http://eprints.soton.ac.uk/id/eprint/485178
ISSN: 1010-6049
PURE UUID: 1c6f6899-29aa-49a3-a759-c985679a1bff
ORCID for Somnath Bar: ORCID iD orcid.org/0000-0003-1679-6130

Catalogue record

Date deposited: 30 Nov 2023 17:55
Last modified: 18 Mar 2024 04:12

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Contributors

Author: Arabinda Maiti
Author: Prasenjit Acharya
Author: Srikanta Sannigrahi
Author: Qi Zhang
Author: Somnath Bar ORCID iD
Author: Suman Chakraborti
Author: Bijoy K. Gayen
Author: Gunadhar Barik
Author: Surajit Ghosh
Author: Milap Punia
Corporate Author: et al.

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